The role of AI in enhancing hospital operational efficiency and patient care

Vishwajit Suryawanshi; Deepika kanyal; Shantanu  sabale; Vikas Bhoyar

doi:10.31893/multirev.2025153

Keywords:

machine learning

data management

personalized medicine

health care

Abstract

Artificial intelligence is changing the way hospitals work by being more efficient and patient-centric. This paper explores how AI technologies, such as machine learning and natural language processing, are integrated into healthcare systems to optimize data management, diagnostics, and personalized medicine. Applications driven by AI streamline workflows, facilitate clinical decision-making, and mitigate human error, thereby improving patient outcomes. Some of the key operational efficiencies that can be facilitated by AI include predictive analytics for better resource allocation, automated scheduling to minimize wait times, and supply chain optimization for effective delivery of medical services.The paper describes how the use of AI is instrumental in transforming patient care through a diagnostic precision, personalized medicine and remote monitoring. The use of ML-algorithm powered diagnostic tools increases image and pattern recognition accuracy through early and accurate detection. Personalized medicine is delivered through AI's capability to personalise treatment plans for individualised patient profiles to improve care outcomes. Remote monitoring of the patient can be done at real-time by using complex sensors and telecommunication tools regarding chronic and critical conditions.Despite these gains, integration barriers, concerns about bias and privacy, and the need for extensive education of healthcare professionals remain. In conclusion, the study emphasizes further innovation and interdisciplinary collaboration toward harnessing the full power of AI in healthcare.This paper also outlines future directions, emphasizing AI's capacity to revolutionize healthcare with advancements in analytics, intelligent hospital frameworks, and clinical research. By aligning technical innovation with ethical considerations, AI promises to create a smarter, patient-centric healthcare ecosystem. The findings advocate for strategic adoption of AI technologies to achieve enhanced operational efficiency, cost reduction, and superior patient care.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Suryawanshi, V., kanyal, D., sabale, S., & Bhoyar, V. (2024). The role of AI in enhancing hospital operational efficiency and patient care . Multidisciplinary Reviews, 8(5), 2025153. https://doi.org/10.31893/multirev.2025153

[1] Achar, S. (2022). Adopting Artificial Intelligence and Deep Learning Techniques in Cloud Computing for Operational Efficiency. https://doi.org/10.5281/zenodo.7439357

[2] Chen, Z.-H., Lin, L., Wu, C.-F., Li, C.-F., Xu, R.-H., & Sun, Y. (2021). Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Communications, 41(11), 1100–1115. https://doi.org/10.1002/cac2.12215

[3] Doris, L., & Potter, K. (2024). Optimizing Hospital Workflows with AI and Robotics. Healthcare.

[4] Farias, F. A. C. de, Dagostini, C. M., Bicca, Y. de A., Falavigna, V. F., & Falavigna, A. (2020). Remote Patient Monitoring: A Systematic Review. Telemedicine and E-Health, 26(5), 576–583. https://doi.org/10.1089/tmj.2019.0066

[5] Jayakumar, S., Sounderajah, V., Normahani, P., Harling, L., Markar, S. R., Ashrafian, H., & Darzi, A. (2022). Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: A meta-research study. Npj Digital Medicine, 5(1), 1–13. https://doi.org/10.1038/s41746-021-00544-y

[6] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101

[7] Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884

[8] Kaur, J., & Mann, K. S. (2018). AI Based HealthCare Platform for Real Time, Predictive and Prescriptive Analytics. In R. Sharma, A. Mantri, & S. Dua (Eds.), Computing, Analytics and Networks (pp. 138–149). Springer. https://doi.org/10.1007/978-981-13-0755-3_11

[9] Kumar, A., Mani, V., Jain, V., Gupta, H., & Venkatesh, V. G. (2023). Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Computers & Industrial Engineering, 175, 108815. https://doi.org/10.1016/j.cie.2022.108815

[10] Layeghian Javan, S., & Sepehri, M. M. (2021). A predictive framework in healthcare: Case study on cardiac arrest prediction. Artificial Intelligence in Medicine, 117, 102099. https://doi.org/10.1016/j.artmed.2021.102099

[11] Lee, D., & Yoon, S. N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271

[12] Long, P., Lu, L., Chen, Q., Chen, Y., Li, C., & Luo, X. (2023). Intelligent selection of healthcare supply chain mode – an applied research based on artificial intelligence. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1310016

[13] M. R., N. S. Z., S., N. A., A. B., N., & M., A. (2021, November 1). THE APPLICATION OF ARTIFICAL INTELLIGENCE (AI) IN HIGHLY INTELLIGENT HOSPITALS. | International Journal of Public Health & Clinical Sciences (IJPHCS) | EBSCOhost. https://openurl.ebsco.com/contentitem/gcd:156920824?sid=ebsco:plink:crawler&id=ebsco:gcd:156920824

[14] Malasinghe, L. P., Ramzan, N., & Dahal, K. (2019). Remote patient monitoring: A comprehensive study. Journal of Ambient Intelligence and Humanized Computing, 10(1), 57–76. https://doi.org/10.1007/s12652-017-0598-x

[15] Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019). Artificial Intelligence Transforms the Future of Health Care. The American Journal of Medicine, 132(7), 795–801. https://doi.org/10.1016/j.amjmed.2019.01.017

[16] Okolo, C. A., Olorunsogo, T., Babawarun, O., Okolo, C. A., Olorunsogo, T., & Babawarun, O. (2024). A comprehensive review of AI applications in personalized medicine. International Journal of Science and Research Archive, 11(1), Article 1. https://doi.org/10.30574/ijsra.2024.11.1.0338

[17] Pacurari, A. C., Bhattarai, S., Muhammad, A., Avram, C., Mederle, A. O., Rosca, O., Bratosin, F., Bogdan, I., Fericean, R. M., Biris, M., Olaru, F., Dumitru, C., Tapalaga, G., & Mavrea, A. (2023). Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review. Diagnostics, 13(13), Article 13. https://doi.org/10.3390/diagnostics13132145

[18] Pierre, K., Haneberg, A. G., Kwak, S., Peters, K. R., Hochhegger, B., Sananmuang, T., Tunlayadechanont, P., Tighe, P. J., Mancuso, A., & Forghani, R. (2023). Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Seminars in Roentgenology, 58(2), 158–169. https://doi.org/10.1053/j.ro.2023.02.003

[19] Pillai, A. S. (2023). AI-enabled Hospital Management Systems for Modern Healthcare: An Analysis of System Components and Interdependencies. Journal of Advanced Analytics in Healthcare Management, 7(1), Article 1.

[20] Pruneski, J. A., Pareek, A., Nwachukwu, B. U., Martin, R. K., Kelly, B. T., Karlsson, J., Pearle, A. D., Kiapour, A. M., & Williams, R. J. (2023). Natural language processing: Using artificial intelligence to understand human language in orthopedics. Knee Surgery, Sports Traumatology, Arthroscopy: Official Journal of the ESSKA, 31(4), 1203–1211. https://doi.org/10.1007/s00167-022-07272-0

[21] Shaheen, M. Y. (2021). Applications of Artificial Intelligence (AI) in healthcare: A review. ScienceOpen Preprints. https://doi.org/10.14293/S2199-1006.1.SOR-.PPVRY8K.v1

[22] Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Mining and Knowledge Discovery, 13(2), e1485. https://doi.org/10.1002/widm.1485

[23] Shiwlani, A., Khan, M., Sherani, A. M. K., Qayyum, M. U., & Hussain, H. K. (2024). REVOLUTIONIZING HEALTHCARE: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PATIENT CARE, DIAGNOSIS, AND TREATMENT. JURIHUM : Jurnal Inovasi Dan Humaniora, 1(5), Article 5.

[24] Win, T., & Phyo, E. E. (2019). Making the Appointment at OPD by Using Clustering Algorithm. International Journal of Science and Engineering Applications, 8(7), 231–235. https://doi.org/10.7753/IJSEA0807.1011

[25] Woodcock, E. W. (2022). Barriers to and Facilitators of Automated Patient Self-scheduling for Health Care Organizations: Scoping Review. Journal of Medical Internet Research, 24(1), e28323. https://doi.org/10.2196/28323

Abstract

References

How to cite